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Record W4402688190 · doi:10.2514/6.2024-3818

Numerical Solution of Transient Thermal Spreading Resistance in Multilayered Flux Tubes

2024· article· en· W4402688190 on OpenAlexaff
Sahar Goudarzi, Lisa Steigerwalt Lam, Yuri S. Muzychka, G.F. Naterer

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSpacecraft and Cryogenic Technologies
Canadian institutionsUniversity of Prince Edward IslandMemorial University of Newfoundland
Fundersnot available
KeywordsTransient (computer programming)Thermal resistanceMaterials scienceTransient analysisThermalMechanicsFlux (metallurgy)Heat fluxTransient responseComputer scienceHeat transferPhysicsThermodynamicsEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

The current study investigates transient thermal spreading within multilayered cylindrical flux tubes using a finite volume numerical model. It examines how thermal spreading occurs, taking into account the size of the heat source and the variations in thickness and thermophysical properties of each material layer. The study evaluates both isoflux and isothermal heat source conditions. Additionally, the findings from this numerical analysis are compared with an analytical approach for isoflux sources from a recent study by the authors. Results are presented for several coatings with varying thermal conductivities. The findings indicate that poor thermal conductors increase the thermal spreading resistance, while good thermal conductors reduce it. This paper provides a deeper understanding of thermal spreading in layered flux tubes and offers useful insights for improving thermal management in various technologies, especially where discrete heat sources are present or where thermal contact conductance occurs at an interface with an applied coating.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.343
Threshold uncertainty score0.262

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.217
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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